Can machine learning models accurately predict myocardial injury after non-cardiac surgery in patients with normal preoperative troponin?
Machine learning models using 6 or 12 perioperative variables can predict myocardial injury after non-cardiac surgery with high accuracy.
Myocardial injury after non-cardiac surgery (MINS) is strongly associated with postoperative outcomes. We developed a prediction model for MINS and have provided it online. Between January 2010 and June 2019, a total of 6811 patients underwent non-cardiac surgery with normal preoperative level of cardiac troponin (cTn). We used machine learning techniques with an extreme gradient boosting algorithm to evaluate the effects of variables on MINS development. We generated two prediction models based on the top 12 and 6 variables. MINS was observed in 1499 (22. 0%) patients. The top 12 variables in descending order according to the effects on MINS are preoperative cTn level, intraoperative inotropic drug infusion, operation duration, emergency operation, operation type, age, high-risk surgery, body mass index, chronic kidney disease, coronary artery disease, intraoperative red blood cell transfusion, and current alcoholic use. The prediction models are available at https: //sjshin. shinyapps. io/minsₒccurₚrediction/. The estimated thresholds were 0. 47 in 12-variable models and 0. 53 in 6-variable models. The areas under the receiver operating characteristic curves are 0. 78 (95% confidence interval CI 0. 77-0. 78) and 0. 77 (95% CI 0. 77-0. 78), respectively, with an accuracy of 0. 97 for both models. Using machine learning techniques, we demonstrated prediction models for MINS. These models require further verification in other populations.
Oh et al. (Thu,) studied this question.